Effect of Carbonate Anions on the Stability of Quaternary Ammonium Groups for Aemfcs
Bibliographic record
Abstract
Anion-exchange membrane fuel cells (AEMFCs) have been attracting significant attention as a promising green and effective technology for energy conversion, suitable for both automotive and stationary applications. AEMFCs operate in an alkaline environment and thus allow the use of non-precious metal electrocatalysts from a wide selection of materials, as well as lower cost anion-exchange membranes (AEMs). In spite of the significant progress recently achieved, the commercial development of AEMFCs is hampered by both AEM degradation and the carbonation processes. The chemical decomposition of the AEMs during fuel cell operation is still considered as the main challenge that needs to be addressed. The combination of high pH environment and high current densities in the AEMFCs results in hydroxide anions with limited solvation, becoming extremely reactive towards positively charged quaternary ammonium (QA) salts. This decomposition leads to detrimental reduction in anion conductivity and therefore in fuel cell performance. Understanding the carbonation process is also critical to allow AEMFCs to operate with ambient air. Hydroxide anions created in the oxygen reduction reaction react with CO2 even at low concentrations, to form bi/carbonates ions. The lower diffusion coefficients and ionic mobility of CO3 -2 and HCO3 - increases resistivity and reduces power output. In this study we experimentally show for the first time the effect of carbonation on the degradation processes of the AEM. The experimental results are compared to modeling by MD. This study provides insights into the carbonation effect on cation stability in alkaline systems, which has significant implications for the final stability of AEMs resulting in long term operation of AEMFCs under real ambient air conditions. References: (1) Ziv, N.; Mustain, W. E.; Dekel, D. R. The Effect of Ambient Carbon Dioxide on Anion-Exchange Membrane Fuel Cells. ChemSusChem 2018, 11 (7), 1136–1150. https://doi.org/10.1002/cssc.201702330. (2) Yassin, K.; Rasin, I. G.; Willdorf-Cohen, S.; Diesendruck, C. E.; Brandon, S.; Dekel, D. R. A Surprising Relation between Operating Temperature and Stability of Anion Exchange Membrane Fuel Cells. J. Power Sources Adv. 2021, 11, 100066. https://doi.org/10.1016/j.powera.2021.100066. (3) Dekel, D. R.; Amar, M.; Willdorf, S.; Kosa, M.; Dhara, S.; Diesendruck, C. E. Effect of Water on the Stability of Quaternary Ammonium Groups for Anion Exchange Membrane Fuel Cell Applications. Chem. Mater. 2017, 29 (10), 4425–4431. https://doi.org/10.1021/acs.chemmater.7b00958. (4) Ziv, N.; Mondal, A. N.; Weissbach, T.; Holdcroft, S.; Dekel, D. R. Effect of CO2 on the Properties of Anion Exchange Membranes for Fuel Cell Applications. J. Memb. Sci. 2019, 586 (March), 140–150. https://doi.org/10.1016/j.memsci.2019.05.053. (5) Srebnik, S.; Pusara, S.; Dekel, D. R. Effect of Carbonate Anions on Quaternary Ammonium-Hydroxide Interaction. J. Phys. Chem. C 2019, 123 (26), 15956–15962. https://doi.org/10.1021/acs.jpcc.9b03131. (6) Vega, J. A.; Mustain, W. E. Effect of CO2, HCO3- and CO3-2 on Oxygen Reduction in Anion Exchange Membrane Fuel Cells. Electrochim. Acta 2010, 55 (5), 1638–1644. https://doi.org/10.1016/j.electacta.2009.10.041. (7) Zelovich, T.; Simari, C.; Nicotera, I.; Dekel, D. R.; Tuckerman, M. E. The Impact of Carbonation on Hydroxide Diffusion in Nano-Confined Anion Exchange Membranes. submitted to J. Materials Chem. A, Jan 29, 2022.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".